Modelling non Measurable Processes by Neural Networks: Forecasting Underground Flow Case Study of the Céze Basin (Gard - France)

Johannet, Anne; Ayral, Pierre Alain; Vayssade, Bernard

doi:10.1007/978-1-4020-6264-3_10

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Several studies conducted in America [3], in Europe [4,5], in Africa [6] and in Algeria [7,8] confirm the best simulation results and forecasting with neural networks. This research work will allow checking efficiency of formal neural networks for flows simulation in semi-arid zone (case of Oued Ouahrane's basin).…”

mentioning

confidence: 86%

Daily rainfall-runoff modelling by neural networks in semi-arid zone: case of Wadi Ouahrane’s basin

Benzineb

Remaoun

2018

J. Fundam and Appl Sci.

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This research work will allow checking efficiency of formal neural networks for flows' modelling of wadi Ouahrane's basin from rainfall-runoff relation which is non-linear. Two models of neural networks were optimized through supervised learning and compared in order to achieve this goal, the first model with input rain, and the second one with rain and input ETP. These neuronal models were compared with another overall model, the GR4j model. Then, it has been optimized and compared with the three first models, a third model of neural network with rain, ETP and soil moisture (calculated by the model GR4j) input.The neuronal models were optimized with algorithm of Levenberg Marquarld (LM), while the GR4j model was optimized with SCE-UA method. The Nash criterion (%) and the correlation coefficient of Pearson (R) allowed appreciating performances of these models.

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mentioning

confidence: 86%

Daily rainfall-runoff modelling by neural networks in semi-arid zone: case of Wadi Ouahrane’s basin

Benzineb

Remaoun

2018

J. Fundam and Appl Sci.

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“…Point measurements of water table levels at the catchment scale (Johannet et al, 2007;Kurtulus and Razack, 2007;Lallahem and Mania, 2003;Minns and Hall, 2004). It has been noted that ANFIS (Takagi and M. Sugeno, 1985;Jang, 1993Jang, , 1995Jang, , 1996Celikyilmaz and Turksen, 2009;Wang et al, 2009) exhibits better simulation performances than classical artificial neural networks (Nayak et al, 2004;El-Shafie et al, 2007;Firat, 2008;Pai et al, 2009;Wang et al, 2009;Maier et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Hydraulic head and groundwater <sup>111</sup>Cd content interpolations using empirical Bayesian kriging (EBK) and geo-adaptive neuro-fuzzy inference system (geo-ANFIS)

Sağır¹,

Kurtuluş²

2017

WSA

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In this study, hydraulic head and 111 Cd interpolations based on the geo-adaptive neuro-fuzzy inference system (Geo-ANFIS) and empirical Bayesian kriging (EBK) were performed for the alluvium unit of Karabağlar Polje in Muğla, Turkey. Hydraulic head measurements and Cd content of groundwater. Both models were applied on the same case study: alluvium of Karabağlar Polje, which covers an area of 25 km 2 in Muğla basin, in the southwest of Turkey. The ANFIS method (called ANFIS XY ) uses two reduced centred pre-processed inputs, which are cartesian coordinates (XY). Geo-ANFIS is tested on a 100-random-data subset of 8 data among 42, with the remaining data used to train and validate the models. ANFIS XY and EBK were then used to interpolate hydraulic head and heavy metal distribution, on a 50 m 2 grid covering the study area for ANFIS XY , while a 100 m 2 grid was used for EBK. Both EBK-and ANFIS XY -simulated hydraulic head and 111 Cd distributions exhibit realistic patterns, with RMSE < 9 m and RMSE < 8 µg/L, respectively. In conclusion, EBK can be considered as a better interpolation method than ANFIS XY for both parameters.

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“…For a few years hydrologists started to apply fuzzy logic to transform an input signal -precipitation -to an output signal -discharge at the outlet of a catchment -with success (Kurtulus and Razack 2007). But only few hydrogeology studies used soft computing to solve their problem (Johannet et al, 2007;Kholghi and Hosseini, 2007). The goal of this work is to compare ordinary kriging (OK) and Adaptive Neuro Fuzzy based Inference System (ANFIS) in their ability to assess a hydraulic head distribution in a complex aquifer system.…”

Section: Introductionmentioning

confidence: 99%

COMPARISON OF ANFIS AND ORDINARY KRIGING TO ASSESS HYDRAULIC HEAD DISTRIBUTION - The Orgeval Case Study

2009

Proceedings of the International Joint Conference on Computational Intelligence

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In this study, two methods are evaluated for assessing hydraulic head distribution in an aquifer unit. These methods consist in Ordinary Kriging (OK) and Adaptive Neuro Fuzzy based Inference System (ANFIS). Both methods are applied on the same case study: a part of the agricultural basin of the Orgeval located 70 km east of Paris, France. 68 samples were used to predict hydraulic head distribution on a 100 m squaregrid. Cartesian coordinates of the samples were used as inputs of the ANFIS, which gives encouraging result. Both simulations have realistic pattern (R 2 > 0.97) even if OK performs slightly better than ANFIS at sampling site. Simulated hydraulic head distributions present discrepancies because the two methods capture different patterns. Combined use of the two approaches allow for improving the sampling location of the observation network.

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Modelling non Measurable Processes by Neural Networks: Forecasting Underground Flow Case Study of the Céze Basin (Gard - France)

Cited by 4 publications

References 16 publications

Daily rainfall-runoff modelling by neural networks in semi-arid zone: case of Wadi Ouahrane’s basin

Daily rainfall-runoff modelling by neural networks in semi-arid zone: case of Wadi Ouahrane’s basin

Hydraulic head and groundwater <sup>111</sup>Cd content interpolations using empirical Bayesian kriging (EBK) and geo-adaptive neuro-fuzzy inference system (geo-ANFIS)

COMPARISON OF ANFIS AND ORDINARY KRIGING TO ASSESS HYDRAULIC HEAD DISTRIBUTION - The Orgeval Case Study

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